Markdown

Markdown is a way to make fancy documents. Make sure you “Knit” often – after every major change – to mitigate errors. Hashtags are how we make levels of a doc.

Next Level

Next level

Fourth level

etc.

Basic formatting

This is bold (that’s double asterisks). Or italics. Or code.

  1. this is a numbered list.
  2. second item.

To add a hyperlink, do [link text](url). For example, go to the Sewanee website.

To link to sections of your document, do [link text](#section-name). For example, go to Markdown. ^keep in mind, no backticks, all lowercase, _ instead of spaces.

To add more vertical spaces between blocks ot text, use this:  , with lines between them.

 

 

Should be more space now.

To include an image – fun! First put an image inside the folder where you Rmd file is located. Then type ![](../../images/full_image_filename_here){width="50%"}.

The ../ backs up your location. So we are able to get something to you in “index.Rms” in “docs” in “test_repo” and pull an image from “images” folder since they are both in “repos” folder.

RMarkdown

RMarkdown is just Markdown with R woven in.

You can do R stuff in the same line as text. For example 6 will show as 6. (That looks like backtick, lower-case-r, space, code, backtick).

You can also do a full-multi-line CHUNK of R stuff: Create with ```{r} on the first line and ``` on the last.

3+3
## [1] 6

Settings to control how R chunks appear:

#this will be shown but not evaluated 
3+3
## [1] 8

Bringing in real data

To manually set working directory while building up my RMarkdown file, just fo to Section > Set working directory > Source file locations.

library(readr)
energy <- read_csv("../../data/IRENA data.csv", skip=1) #`skip=` to skip the first row at the top that is not actual data 
## Rows: 67200 Columns: 6
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): Country/area, Technology, Data Type, Grid connection, Electricity s...
## dbl (1): Year
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
energy %>% names
## [1] "Country/area"           "Technology"             "Data Type"             
## [4] "Grid connection"        "Year"                   "Electricity statistics"
energy %>% head
## # A tibble: 6 × 6
##   `Country/area` Technology      `Data Type`             `Grid connection`  Year
##   <chr>          <chr>           <chr>                   <chr>             <dbl>
## 1 Afghanistan    Total renewable Electricity Generation… On-grid            2000
## 2 Afghanistan    Total renewable Electricity Generation… On-grid            2001
## 3 Afghanistan    Total renewable Electricity Generation… On-grid            2002
## 4 Afghanistan    Total renewable Electricity Generation… On-grid            2003
## 5 Afghanistan    Total renewable Electricity Generation… On-grid            2004
## 6 Afghanistan    Total renewable Electricity Generation… On-grid            2005
## # ℹ 1 more variable: `Electricity statistics` <chr>
energy %>% tail
## # A tibble: 6 × 6
##   `Country/area` Technology `Data Type`                  `Grid connection`  Year
##   <chr>          <chr>      <chr>                        <chr>             <dbl>
## 1 Zimbabwe       Nuclear    Electricity Generation (GWh) On-grid            2019
## 2 Zimbabwe       Nuclear    Electricity Generation (GWh) On-grid            2020
## 3 Zimbabwe       Nuclear    Electricity Generation (GWh) On-grid            2021
## 4 Zimbabwe       Nuclear    Electricity Generation (GWh) On-grid            2022
## 5 Zimbabwe       Nuclear    Electricity Generation (GWh) On-grid            2023
## 6 Zimbabwe       Nuclear    Electricity Generation (GWh) On-grid            2024
## # ℹ 1 more variable: `Electricity statistics` <chr>
energy %>% pull(Technology) %>% unique %>% sort
##  [1] "Biogas"               "Coal and peat"        "Geothermal energy"   
##  [4] "Natural gas"          "Nuclear"              "Offshore wind energy"
##  [7] "Oil"                  "Onshore wind energy"  "Renewable hydropower"
## [10] "Solar photovoltaic"   "Total non-renewable"  "Total renewable"

Filtering code now

Here is a new section where I talk about what I’s doing.

#filter data
fenergy <- energy %>% 
  filter(`Country/area` == "Germany")
library(ggplot2)
library(plotly)
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout
p <- ggplot(fenergy,
       aes(x = Year,
           y = as.numeric(`Electricity statistics`),
           fill = Technology))+
  geom_area()
ggplotly(p)
## Warning in FUN(X[[i]], ...): NAs introduced by coercion
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_align()`).

Bringing in new datastes